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<div class="highlight"><pre><span class="c">#!/usr/bin/python</span>
<span class="c"># The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt</span>
<span class="c">#</span>
<span class="c">#   This example program shows how you can use dlib to make an object detector</span>
<span class="c">#   for things like faces, pedestrians, and any other semi-rigid object.  In</span>
<span class="c">#   particular, we go though the steps to train the kind of sliding window</span>
<span class="c">#   object detector first published by Dalal and Triggs in 2005 in the paper</span>
<span class="c">#   Histograms of Oriented Gradients for Human Detection.  </span>
<span class="c">#</span>
<span class="c">#</span>
<span class="c"># COMPILING THE DLIB PYTHON INTERFACE</span>
<span class="c">#   Dlib comes with a compiled python interface for python 2.7 on MS Windows.  If</span>
<span class="c">#   you are using another python version or operating system then you need to</span>
<span class="c">#   compile the dlib python interface before you can use this file.  To do this,</span>
<span class="c">#   run compile_dlib_python_module.bat.  This should work on any operating system</span>
<span class="c">#   so long as you have CMake and boost-python installed.  On Ubuntu, this can be</span>
<span class="c">#   done easily by running the command:  sudo apt-get install libboost-python-dev cmake</span>

<span class="kn">import</span> <span class="nn">dlib</span><span class="o">,</span> <span class="nn">sys</span><span class="o">,</span> <span class="nn">glob</span>
<span class="kn">from</span> <span class="nn">skimage</span> <span class="kn">import</span> <span class="n">io</span>

<span class="c"># In this example we are going to train a face detector based on the small</span>
<span class="c"># faces dataset in the examples/faces directory.  This means you need to supply</span>
<span class="c"># the path to this faces folder as a command line argument so we will know</span>
<span class="c"># where it is.</span>
<span class="k">if</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">):</span>
    <span class="k">print</span> <span class="s">&quot;Give the path to the examples/faces directory as the argument to this&quot;</span>
    <span class="k">print</span> <span class="s">&quot;program.  For example, if you are in the python_examples folder then &quot;</span>
    <span class="k">print</span> <span class="s">&quot;execute this program by running:&quot;</span>
    <span class="k">print</span> <span class="s">&quot;  ./<a href="train_object_detector.py.html">train_object_detector.py</a> ../examples/faces&quot;</span>
    <span class="nb">exit</span><span class="p">()</span>
<span class="n">faces_folder</span> <span class="o">=</span> <span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>


<span class="c"># Now let&#39;s do the training.  The train_simple_object_detector() function has a</span>
<span class="c"># bunch of options, all of which come with reasonable default values.  The next</span>
<span class="c"># few lines goes over some of these options.</span>
<span class="n">options</span> <span class="o">=</span> <span class="n">dlib</span><span class="o">.</span><span class="n">simple_object_detector_training_options</span><span class="p">()</span>
<span class="c"># Since faces are left/right symmetric we can tell the trainer to train a</span>
<span class="c"># symmetric detector.  This helps it get the most value out of the training</span>
<span class="c"># data.</span>
<span class="n">options</span><span class="o">.</span><span class="n">add_left_right_image_flips</span> <span class="o">=</span> <span class="bp">True</span>
<span class="c"># The trainer is a kind of support vector machine and therefore has the usual</span>
<span class="c"># SVM C parameter.  In general, a bigger C encourages it to fit the training</span>
<span class="c"># data better but might lead to overfitting.  You must find the best C value</span>
<span class="c"># empirically by checking how well the trained detector works on a test set of</span>
<span class="c"># images you haven&#39;t trained on.  Don&#39;t just leave the value set at 5.  Try a</span>
<span class="c"># few different C values and see what works best for your data.</span>
<span class="n">options</span><span class="o">.</span><span class="n">C</span> <span class="o">=</span> <span class="mi">5</span> 
<span class="c"># Tell the code how many CPU cores your computer has for the fastest training.</span>
<span class="n">options</span><span class="o">.</span><span class="n">num_threads</span> <span class="o">=</span> <span class="mi">4</span>
<span class="n">options</span><span class="o">.</span><span class="n">be_verbose</span> <span class="o">=</span> <span class="bp">True</span> 

<span class="c"># This function does the actual training.  It will save the final detector to</span>
<span class="c"># detector.svm.  The input is an XML file that lists the images in the training</span>
<span class="c"># dataset and also contains the positions of the face boxes.  To create your</span>
<span class="c"># own XML files you can use the imglab tool which can be found in the</span>
<span class="c"># tools/imglab folder.  It is a simple graphical tool for labeling objects in</span>
<span class="c"># images with boxes.  To see how to use it read the tools/imglab/README.txt</span>
<span class="c"># file.  But for this example, we just use the training.xml file included with</span>
<span class="c"># dlib.</span>
<span class="n">dlib</span><span class="o">.</span><span class="n">train_simple_object_detector</span><span class="p">(</span><span class="n">faces_folder</span><span class="o">+</span><span class="s">&quot;/training.xml&quot;</span><span class="p">,</span><span class="s">&quot;detector.svm&quot;</span><span class="p">,</span> <span class="n">options</span><span class="p">)</span>



<span class="c"># Now that we have a face detector we can test it.  The first statement tests</span>
<span class="c"># it on the training data.  It will print the precision, recall, and then</span>
<span class="c"># average precision.</span>
<span class="k">print</span> <span class="s">&quot;</span><span class="se">\n</span><span class="s">training accuracy:&quot;</span><span class="p">,</span> <span class="n">dlib</span><span class="o">.</span><span class="n">test_simple_object_detector</span><span class="p">(</span><span class="n">faces_folder</span><span class="o">+</span><span class="s">&quot;/training.xml&quot;</span><span class="p">,</span> <span class="s">&quot;detector.svm&quot;</span><span class="p">)</span>
<span class="c"># However, to get an idea if it really worked without overfitting we need to</span>
<span class="c"># run it on images it wasn&#39;t trained on.  The next line does this.  Happily, we</span>
<span class="c"># see that the object detector works perfectly on the testing images.</span>
<span class="k">print</span> <span class="s">&quot;testing accuracy: &quot;</span><span class="p">,</span> <span class="n">dlib</span><span class="o">.</span><span class="n">test_simple_object_detector</span><span class="p">(</span><span class="n">faces_folder</span><span class="o">+</span><span class="s">&quot;/testing.xml&quot;</span><span class="p">,</span> <span class="s">&quot;detector.svm&quot;</span><span class="p">)</span>



<span class="c"># Now let&#39;s use the detector as you would in a normal application.  First we</span>
<span class="c"># will load it from disk.</span>
<span class="n">detector</span> <span class="o">=</span> <span class="n">dlib</span><span class="o">.</span><span class="n">simple_object_detector</span><span class="p">(</span><span class="s">&quot;detector.svm&quot;</span><span class="p">)</span>

<span class="c"># We can look at the HOG filter we learned.  It should look like a face.  Neat!</span>
<span class="n">win_det</span> <span class="o">=</span> <span class="n">dlib</span><span class="o">.</span><span class="n">image_window</span><span class="p">()</span>
<span class="n">win_det</span><span class="o">.</span><span class="n">set_image</span><span class="p">(</span><span class="n">detector</span><span class="p">)</span>

<span class="c"># Now let&#39;s run the detector over the images in the faces folder and display the</span>
<span class="c"># results.</span>
<span class="k">print</span> <span class="s">&quot;</span><span class="se">\n</span><span class="s">Showing detections on the images in the faces folder...&quot;</span>
<span class="n">win</span> <span class="o">=</span> <span class="n">dlib</span><span class="o">.</span><span class="n">image_window</span><span class="p">()</span>
<span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">faces_folder</span><span class="o">+</span><span class="s">&quot;/*.jpg&quot;</span><span class="p">):</span>
    <span class="k">print</span> <span class="s">&quot;processing file:&quot;</span><span class="p">,</span> <span class="n">f</span>
    <span class="n">img</span> <span class="o">=</span> <span class="n">io</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
    <span class="n">dets</span> <span class="o">=</span> <span class="n">detector</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>
    <span class="k">print</span> <span class="s">&quot;number of faces detected:&quot;</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">dets</span><span class="p">)</span>
    <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">dets</span><span class="p">:</span>
        <span class="k">print</span> <span class="s">&quot;  detection position left,top,right,bottom:&quot;</span><span class="p">,</span> <span class="n">d</span><span class="o">.</span><span class="n">left</span><span class="p">(),</span> <span class="n">d</span><span class="o">.</span><span class="n">top</span><span class="p">(),</span> <span class="n">d</span><span class="o">.</span><span class="n">right</span><span class="p">(),</span> <span class="n">d</span><span class="o">.</span><span class="n">bottom</span><span class="p">()</span>

    <span class="n">win</span><span class="o">.</span><span class="n">clear_overlay</span><span class="p">()</span>
    <span class="n">win</span><span class="o">.</span><span class="n">set_image</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>
    <span class="n">win</span><span class="o">.</span><span class="n">add_overlay</span><span class="p">(</span><span class="n">dets</span><span class="p">)</span>
    <span class="nb">raw_input</span><span class="p">(</span><span class="s">&quot;Hit enter to continue&quot;</span><span class="p">)</span>
</pre></div>
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